AI-powered oil spill prediction systems are showing significant promise in enhancing the accuracy of emergency response efforts, with potential improvements of up to 25% in forecasting outcomes. Oil spills pose some of the most severe environmental threats, capable of devastating marine life, harming coastal economies, and causing long-term ecological damage. While traditional models like MEDSLIK-II have long been used to simulate how oil spreads and degrades in seawater, their effectiveness has often been constrained by the need for expert-driven calibration of key physical parameters. These manual tuning processes, though grounded in experience, struggle to account for the dynamic and unpredictable nature of real-world ocean currents, wind patterns, and wave behavior. As a result, predictions can lag behind actual spill developments, reducing the effectiveness of containment and cleanup operations. Recent advances in artificial intelligence are helping to overcome these limitations. By training machine learning models on vast datasets of historical spill events, satellite observations, and environmental conditions, AI systems can identify complex patterns and relationships that are difficult for human experts to detect. These models can dynamically adjust predictions in real time, incorporating live data from weather systems, ocean sensors, and satellite imagery to produce more accurate and responsive forecasts. Early tests have demonstrated that AI-enhanced models can improve prediction accuracy by up to 25% compared to traditional approaches, particularly in complex coastal environments where currents and winds interact unpredictably. This increased precision allows emergency responders to deploy containment booms, skimmers, and other mitigation tools more effectively, minimizing environmental damage and reducing cleanup costs. Moreover, AI systems can simulate multiple possible spill scenarios under varying conditions, helping agencies prepare for a wider range of outcomes. This proactive planning capability strengthens resilience and enables faster, better-informed decision-making during crises. As climate change intensifies extreme weather and increases shipping activity in vulnerable regions, the need for reliable oil spill prediction tools has never been greater. With AI bridging the gap between theoretical modeling and real-world complexity, the next generation of response systems promises to be faster, smarter, and more effective in protecting marine ecosystems and coastal communities.